{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Import all Packages"
      ],
      "metadata": {
        "id": "ZfX9I4zwNdM9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "vBzwpSkOtPAe"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from PIL import Image\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torchvision import transforms\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tqdm import tqdm"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preparation\n",
        "1. We will be using the model `vit_base_patch16_224`\n",
        "  * This means all image need to be reshaped to (224,224)\n",
        "2. In training, we are using data augmentation process: `RandomHorizontalFlip`. \n",
        "  * It is optional step\n",
        "  *  It randomly flips an input image horizontally\n",
        "  * It  artificially increase the size and diversity of the training dataset\n",
        "3. We also normalizes the image `transforms.Normalize`\n",
        "  * This will reduce the scale of image\n",
        "  * It helps on Faster convergence, better generalization, and numercial stability\n"
      ],
      "metadata": {
        "id": "-hieKe9SfUWN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Please download the images from `https://www.kaggle.com/datasets/jr2ngb/cataractdataset` and place in your machine's directory\n",
        "DATA_DIR = \"/Users/premtimsina/Documents/chapter7/dataset/\"\n",
        "CLASSES = [\"normal\", \"cataract\", \"glaucoma\", \"retina_disease\"]\n",
        "\n",
        "data = []\n",
        "for class_idx, class_name in enumerate(CLASSES):\n",
        "    class_dir = os.path.join(DATA_DIR, class_name)\n",
        "    for img_name in os.listdir(class_dir):\n",
        "        img_path = os.path.join(class_dir, img_name)\n",
        "        data.append([img_path, class_idx])\n",
        "\n",
        "df = pd.DataFrame(data, columns=[\"image_path\", \"label\"])\n",
        "train_df, test_df = train_test_split(df, test_size=0.2, random_state=42, stratify=df[\"label\"])\n",
        "\n",
        "train_transforms = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
        "])\n",
        "\n",
        "test_transforms = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
        "])\n",
        "\n",
        "class CataractDataset(Dataset):\n",
        "    def __init__(self, image_paths, labels, transform=None):\n",
        "        self.image_paths = image_paths\n",
        "        self.labels = labels\n",
        "        self.transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.image_paths)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        img_path = self.image_paths[idx]\n",
        "        label = self.labels[idx]\n",
        "        img = Image.open(img_path).convert(\"RGB\")\n",
        "\n",
        "        if self.transform:\n",
        "            img = self.transform(img)\n",
        "\n",
        "        return img, label\n",
        "\n",
        "train_dataset = CataractDataset(train_df[\"image_path\"].values, train_df[\"label\"].values, transform=train_transforms)\n",
        "test_dataset = CataractDataset(test_df[\"image_path\"].values, test_df[\"label\"].values, transform=test_transforms)\n",
        "\n",
        "train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True)\n",
        "test_loader = DataLoader(test_dataset, batch_size=4, shuffle=False)\n",
        "\n"
      ],
      "metadata": {
        "id": "Onl2qdgttxX6"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Let's review our dataloader\n",
        "1. We are just viewing the image from train_dataloader\n",
        "2. There is one very important step:\n",
        "  * matplotlib need the image in [H, W, C]; where dataloader has image of shape [C, H,W]\n",
        "  * Thus, before plotting we are transposing so that the data is suitable for viewing for matplotlib"
      ],
      "metadata": {
        "id": "-1253g1TPL6i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torchvision.transforms as transforms\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "data_iter = iter(train_loader)\n",
        "images, labels = next(data_iter)\n",
        "\n",
        "# Function to unnormalize and convert a tensor image to numpy array\n",
        "def imshow(img_tensor):\n",
        "    img = img_tensor.numpy()\n",
        "    # transposing\n",
        "    img = np.transpose(img, (1, 2, 0)) \n",
        "    mean = np.array([0.485, 0.456, 0.406])\n",
        "    std = np.array([0.229, 0.224, 0.225])\n",
        "    img = std * img + mean\n",
        "    img = np.clip(img, 0, 1)\n",
        "    return img\n",
        "\n",
        "# Display the images and their labels\n",
        "fig, axes = plt.subplots(1, len(images), figsize=(12, 12))\n",
        "\n",
        "for idx, (image, label) in enumerate(zip(images, labels)):\n",
        "    axes[idx].imshow(imshow(image))\n",
        "    axes[idx].set_title(f\"Label: {label.item()}\")\n",
        "    axes[idx].axis(\"off\")\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        },
        "id": "rD9vNXxBLM2g",
        "outputId": "822e300d-5738-4f8e-8050-37a594ade2a4"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Declare the model\n",
        "1. We are using the pre-trained model from timm\n",
        "2. If the image is color, \n",
        "  * in_chans=3;\n",
        "3. if image is graysclae, \n",
        "  * in_chans=3=1\n",
        "4. We need to declare, how many classes we have in out dataset: \n",
        "  * For our case, it was 4.\n",
        "  * num_classes=4\n",
        "5. `pretrained=True`\n",
        "  * We are mentioning that we want weight of pre-trained model. If you want to train from scratch, you could just mention \n",
        "    * pretrained=False"
      ],
      "metadata": {
        "id": "ySxcYUlrQCyh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import timm\n",
        "model = timm.create_model(\"vit_base_patch16_224\", in_chans=3, num_classes=4, pretrained=True)\n"
      ],
      "metadata": {
        "id": "48WE3Cmntu_M"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Declare the train and test loop\n",
        "1. In the below code: I am converting output and target into 'cpu'. Somehow my M1 chip is producing the problem. You may not encounter the problem. Thus, this is totally optional step."
      ],
      "metadata": {
        "id": "4o3rjbj4QLGT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def train(model, device, train_loader, optimizer, criterion, epoch, accelerator):\n",
        "    model.train()\n",
        "    running_loss = 0.0\n",
        "\n",
        "    for batch_idx, (data, target) in enumerate(tqdm(train_loader)):\n",
        "        data, target = data.to(device), target.to(device)\n",
        "        optimizer.zero_grad()\n",
        "        output = model(data)\n",
        "        loss = criterion(output, target)\n",
        "        accelerator.backward(loss)\n",
        "        optimizer.step()\n",
        "        running_loss += loss.item()\n",
        "\n",
        "    avg_loss = running_loss / len(train_loader)\n",
        "    print(f\"Epoch: {epoch}, Loss: {avg_loss:.4f}\")\n",
        "\n",
        "from sklearn.metrics import confusion_matrix, recall_score, precision_score\n",
        "\n",
        "def test(model, device, test_loader, criterion, accelerator):\n",
        "    model.eval()\n",
        "    test_loss = 0\n",
        "    correct = 0\n",
        "    all_preds = []\n",
        "    all_targets = []\n",
        "    \n",
        "    with torch.no_grad():\n",
        "        for data, target in test_loader:\n",
        "            data, target = data.to(device), target.to(device)\n",
        "            output = model(data)\n",
        "            test_loss += criterion(output, target).item()\n",
        "            output_cpu = output.to('cpu')\n",
        "            target_cpu=target.to('cpu')\n",
        "            pred = output_cpu.argmax(dim=1, keepdim=True)\n",
        "            correct += pred.eq(target_cpu.view_as(pred)).sum().item()\n",
        "            \n",
        "            all_preds.extend(pred.flatten().tolist())\n",
        "            all_targets.extend(target.flatten().tolist())\n",
        "\n",
        "    test_loss /= len(test_loader)\n",
        "    accuracy = 100. * correct / len(test_loader.dataset)\n",
        "    print(f\"Test Loss: {test_loss:.4f}, Accuracy: {accuracy:.2f}%\")\n",
        "    \n",
        "    # Calculate confusion matrix, sensitivity (recall), and specificity\n",
        "    cm = confusion_matrix(all_targets, all_preds)\n",
        "    sensitivity = recall_score(all_targets, all_preds, average=None)\n",
        "    specificity = (cm.sum(axis=0) - cm.diagonal()) / cm.sum(axis=0)\n",
        "    \n",
        "    for i, (sens, spec) in enumerate(zip(sensitivity, specificity)):\n",
        "        print(f\"Class {i}: Sensitivity (Recall): {sens:.4f}, Specificity: {spec:.4f}\")\n",
        "\n"
      ],
      "metadata": {
        "id": "LiSLKNCqt48G"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from accelerate import Accelerator\n",
        "from torch.optim import Adam\n",
        "\n",
        "accelerator = Accelerator()\n",
        "device = accelerator.device\n",
        "learning_rate = 1e-4\n",
        "\n",
        "optimizer = Adam(model.parameters(), lr=learning_rate)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "train_loader, test_loader = accelerator.prepare(train_loader, test_loader)\n",
        "model, optimizer, criterion = accelerator.prepare(model, optimizer, criterion)\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "nTzHALszt_0I"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "num_epochs = 50\n",
        "for epoch in range(1, num_epochs + 1):\n",
        "    train(model, device, train_loader, optimizer, criterion, epoch, accelerator)\n",
        "    test(model, device, test_loader, criterion, accelerator)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M26lZZr66wWE",
        "outputId": "c4147988-57b3-49e4-baca-f47bef767fb1"
      },
      "execution_count": 22,
      "outputs": [
        {
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        {
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          "text": [
            "Epoch: 1, Loss: 0.9448\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "/var/folders/dt/v286l_1506x752dg9dwh_yl40000gn/T/ipykernel_57405/263114855.py:46: RuntimeWarning: invalid value encountered in divide\n",
            "  specificity = (cm.sum(axis=0) - cm.diagonal()) / cm.sum(axis=0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 1.1269, Accuracy: 48.76%\n",
            "Class 0: Sensitivity (Recall): 0.6066, Specificity: 0.3509\n",
            "Class 1: Sensitivity (Recall): 0.7500, Specificity: 0.4643\n",
            "Class 2: Sensitivity (Recall): 0.0000, Specificity: nan\n",
            "Class 3: Sensitivity (Recall): 0.3500, Specificity: 0.8056\n"
          ]
        },
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          "text": [
            "Epoch: 2, Loss: 0.9016\n",
            "Test Loss: 1.0736, Accuracy: 53.72%\n",
            "Class 0: Sensitivity (Recall): 0.7049, Specificity: 0.3582\n",
            "Class 1: Sensitivity (Recall): 0.5500, Specificity: 0.5000\n",
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            "Class 3: Sensitivity (Recall): 0.0000, Specificity: 1.0000\n"
          ]
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          "text": [
            "Epoch: 3, Loss: 0.8900\n",
            "Test Loss: 1.0879, Accuracy: 58.68%\n",
            "Class 0: Sensitivity (Recall): 0.9344, Specificity: 0.4300\n",
            "Class 1: Sensitivity (Recall): 0.4500, Specificity: 0.2500\n",
            "Class 2: Sensitivity (Recall): 0.1000, Specificity: 0.0000\n",
            "Class 3: Sensitivity (Recall): 0.1500, Specificity: 0.5714\n"
          ]
        },
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          "text": [
            "Epoch: 4, Loss: 0.8520\n",
            "Test Loss: 1.2384, Accuracy: 46.28%\n",
            "Class 0: Sensitivity (Recall): 0.4098, Specificity: 0.2857\n",
            "Class 1: Sensitivity (Recall): 0.5500, Specificity: 0.3529\n",
            "Class 2: Sensitivity (Recall): 0.7500, Specificity: 0.7222\n",
            "Class 3: Sensitivity (Recall): 0.2500, Specificity: 0.6667\n"
          ]
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          "text": [
            "Epoch: 5, Loss: 0.8687\n",
            "Test Loss: 0.9601, Accuracy: 61.16%\n",
            "Class 0: Sensitivity (Recall): 0.7869, Specificity: 0.3425\n",
            "Class 1: Sensitivity (Recall): 0.5500, Specificity: 0.4500\n",
            "Class 2: Sensitivity (Recall): 0.7500, Specificity: 0.4444\n",
            "Class 3: Sensitivity (Recall): 0.0000, Specificity: 1.0000\n"
          ]
        },
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          "text": [
            "Epoch: 6, Loss: 0.8112\n",
            "Test Loss: 1.0747, Accuracy: 57.02%\n",
            "Class 0: Sensitivity (Recall): 0.9016, Specificity: 0.4388\n",
            "Class 1: Sensitivity (Recall): 0.5500, Specificity: 0.2667\n",
            "Class 2: Sensitivity (Recall): 0.1500, Specificity: 0.2500\n",
            "Class 3: Sensitivity (Recall): 0.0000, Specificity: 1.0000\n"
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        },
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            "Epoch: 7, Loss: 0.6973\n",
            "Test Loss: 1.0880, Accuracy: 60.33%\n",
            "Class 0: Sensitivity (Recall): 0.8033, Specificity: 0.3797\n",
            "Class 1: Sensitivity (Recall): 0.6000, Specificity: 0.4545\n",
            "Class 2: Sensitivity (Recall): 0.5500, Specificity: 0.3889\n",
            "Class 3: Sensitivity (Recall): 0.0500, Specificity: 0.5000\n"
          ]
        },
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          "text": [
            "Epoch: 8, Loss: 0.7467\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "/var/folders/dt/v286l_1506x752dg9dwh_yl40000gn/T/ipykernel_57405/263114855.py:46: RuntimeWarning: invalid value encountered in divide\n",
            "  specificity = (cm.sum(axis=0) - cm.diagonal()) / cm.sum(axis=0)\n"
          ]
        },
        {
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          "text": [
            "Test Loss: 1.2283, Accuracy: 52.89%\n",
            "Class 0: Sensitivity (Recall): 0.5410, Specificity: 0.3529\n",
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            "Class 3: Sensitivity (Recall): 0.0000, Specificity: nan\n"
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            "Epoch: 9, Loss: 0.7473\n",
            "Test Loss: 1.2848, Accuracy: 54.55%\n",
            "Class 0: Sensitivity (Recall): 0.8361, Specificity: 0.3780\n",
            "Class 1: Sensitivity (Recall): 0.3000, Specificity: 0.1429\n",
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            "Class 3: Sensitivity (Recall): 0.2000, Specificity: 0.8261\n"
          ]
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          "text": [
            "Epoch: 10, Loss: 0.7980\n",
            "Test Loss: 1.0402, Accuracy: 59.50%\n",
            "Class 0: Sensitivity (Recall): 0.7213, Specificity: 0.3433\n",
            "Class 1: Sensitivity (Recall): 0.5500, Specificity: 0.3889\n",
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            "Class 3: Sensitivity (Recall): 0.1500, Specificity: 0.6250\n"
          ]
        },
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          "text": [
            "Epoch: 11, Loss: 0.6878\n",
            "Test Loss: 1.1001, Accuracy: 57.85%\n",
            "Class 0: Sensitivity (Recall): 0.7213, Specificity: 0.3333\n",
            "Class 1: Sensitivity (Recall): 0.6000, Specificity: 0.5000\n",
            "Class 2: Sensitivity (Recall): 0.6000, Specificity: 0.4783\n",
            "Class 3: Sensitivity (Recall): 0.1000, Specificity: 0.7500\n"
          ]
        },
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          "text": [
            "Epoch: 12, Loss: 0.6879\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
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            "  specificity = (cm.sum(axis=0) - cm.diagonal()) / cm.sum(axis=0)\n"
          ]
        },
        {
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          "text": [
            "Test Loss: 1.2598, Accuracy: 57.02%\n",
            "Class 0: Sensitivity (Recall): 0.9344, Specificity: 0.4519\n",
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            "Test Loss: 1.2294, Accuracy: 49.59%\n",
            "Class 0: Sensitivity (Recall): 0.4918, Specificity: 0.3617\n",
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            "Test Loss: 1.3928, Accuracy: 57.02%\n",
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            "Test Loss: 1.2990, Accuracy: 54.55%\n",
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            "Test Loss: 1.1137, Accuracy: 55.37%\n",
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            "Test Loss: 1.1910, Accuracy: 66.12%\n",
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            "Test Loss: 1.3796, Accuracy: 53.72%\n",
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            "Test Loss: 1.1983, Accuracy: 57.02%\n",
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            "Test Loss: 1.3232, Accuracy: 53.72%\n",
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            "Test Loss: 1.5208, Accuracy: 46.28%\n",
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            "Test Loss: 1.4732, Accuracy: 54.55%\n",
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            "Test Loss: 1.5196, Accuracy: 47.11%\n",
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            "Test Loss: 1.4471, Accuracy: 57.02%\n",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 29, Loss: 0.3730\n",
            "Test Loss: 1.4943, Accuracy: 54.55%\n",
            "Class 0: Sensitivity (Recall): 0.6721, Specificity: 0.4225\n",
            "Class 1: Sensitivity (Recall): 0.8000, Specificity: 0.4286\n",
            "Class 2: Sensitivity (Recall): 0.3000, Specificity: 0.4000\n",
            "Class 3: Sensitivity (Recall): 0.1500, Specificity: 0.7500\n"
          ]
        },
        {
          "output_type": "stream",
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          "text": [
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 30, Loss: 0.2675\n",
            "Test Loss: 1.9772, Accuracy: 53.72%\n",
            "Class 0: Sensitivity (Recall): 0.5902, Specificity: 0.3793\n",
            "Class 1: Sensitivity (Recall): 0.7500, Specificity: 0.4000\n",
            "Class 2: Sensitivity (Recall): 0.4000, Specificity: 0.4667\n",
            "Class 3: Sensitivity (Recall): 0.3000, Specificity: 0.7391\n"
          ]
        },
        {
          "output_type": "stream",
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          "text": [
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 31, Loss: 0.4079\n",
            "Test Loss: 1.2920, Accuracy: 55.37%\n",
            "Class 0: Sensitivity (Recall): 0.7049, Specificity: 0.3175\n",
            "Class 1: Sensitivity (Recall): 0.5500, Specificity: 0.3529\n",
            "Class 2: Sensitivity (Recall): 0.6000, Specificity: 0.5556\n",
            "Class 3: Sensitivity (Recall): 0.0500, Specificity: 0.9286\n"
          ]
        },
        {
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        },
        {
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          "name": "stdout",
          "text": [
            "Epoch: 32, Loss: 0.2767\n",
            "Test Loss: 1.5353, Accuracy: 56.20%\n",
            "Class 0: Sensitivity (Recall): 0.6393, Specificity: 0.2909\n",
            "Class 1: Sensitivity (Recall): 0.5000, Specificity: 0.4118\n",
            "Class 2: Sensitivity (Recall): 0.6500, Specificity: 0.5185\n",
            "Class 3: Sensitivity (Recall): 0.3000, Specificity: 0.7273\n"
          ]
        },
        {
          "output_type": "stream",
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          "text": [
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        },
        {
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          "name": "stdout",
          "text": [
            "Epoch: 33, Loss: 0.2330\n",
            "Test Loss: 1.7612, Accuracy: 42.98%\n",
            "Class 0: Sensitivity (Recall): 0.3934, Specificity: 0.3684\n",
            "Class 1: Sensitivity (Recall): 0.7000, Specificity: 0.4167\n",
            "Class 2: Sensitivity (Recall): 0.3500, Specificity: 0.6111\n",
            "Class 3: Sensitivity (Recall): 0.3500, Specificity: 0.8293\n"
          ]
        },
        {
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          "text": [
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 34, Loss: 0.3126\n",
            "Test Loss: 1.4320, Accuracy: 55.37%\n",
            "Class 0: Sensitivity (Recall): 0.8033, Specificity: 0.3951\n",
            "Class 1: Sensitivity (Recall): 0.2500, Specificity: 0.4444\n",
            "Class 2: Sensitivity (Recall): 0.6000, Specificity: 0.4783\n",
            "Class 3: Sensitivity (Recall): 0.0500, Specificity: 0.8750\n"
          ]
        },
        {
          "output_type": "stream",
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          "text": [
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 35, Loss: 0.2884\n",
            "Test Loss: 1.7068, Accuracy: 47.93%\n",
            "Class 0: Sensitivity (Recall): 0.6393, Specificity: 0.4091\n",
            "Class 1: Sensitivity (Recall): 0.6000, Specificity: 0.4783\n",
            "Class 2: Sensitivity (Recall): 0.1500, Specificity: 0.6667\n",
            "Class 3: Sensitivity (Recall): 0.2000, Specificity: 0.8261\n"
          ]
        },
        {
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        },
        {
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          "name": "stdout",
          "text": [
            "Epoch: 36, Loss: 0.3131\n",
            "Test Loss: 1.5639, Accuracy: 58.68%\n",
            "Class 0: Sensitivity (Recall): 0.8197, Specificity: 0.3827\n",
            "Class 1: Sensitivity (Recall): 0.6000, Specificity: 0.4286\n",
            "Class 2: Sensitivity (Recall): 0.1500, Specificity: 0.6250\n",
            "Class 3: Sensitivity (Recall): 0.3000, Specificity: 0.4545\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 37, Loss: 0.2329\n",
            "Test Loss: 1.7258, Accuracy: 46.28%\n",
            "Class 0: Sensitivity (Recall): 0.4590, Specificity: 0.3488\n",
            "Class 1: Sensitivity (Recall): 0.5500, Specificity: 0.3529\n",
            "Class 2: Sensitivity (Recall): 0.7000, Specificity: 0.6889\n",
            "Class 3: Sensitivity (Recall): 0.1500, Specificity: 0.8125\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 38, Loss: 0.1365\n",
            "Test Loss: 2.2104, Accuracy: 48.76%\n",
            "Class 0: Sensitivity (Recall): 0.4918, Specificity: 0.3333\n",
            "Class 1: Sensitivity (Recall): 0.7500, Specificity: 0.5000\n",
            "Class 2: Sensitivity (Recall): 0.3000, Specificity: 0.6250\n",
            "Class 3: Sensitivity (Recall): 0.4000, Specificity: 0.7333\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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        },
        {
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          "name": "stdout",
          "text": [
            "Epoch: 39, Loss: 0.2390\n",
            "Test Loss: 1.6950, Accuracy: 60.33%\n",
            "Class 0: Sensitivity (Recall): 0.6393, Specificity: 0.2909\n",
            "Class 1: Sensitivity (Recall): 0.9500, Specificity: 0.4722\n",
            "Class 2: Sensitivity (Recall): 0.4000, Specificity: 0.4667\n",
            "Class 3: Sensitivity (Recall): 0.3500, Specificity: 0.5333\n"
          ]
        },
        {
          "output_type": "stream",
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          "text": [
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        },
        {
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          "name": "stdout",
          "text": [
            "Epoch: 40, Loss: 0.1917\n",
            "Test Loss: 1.9211, Accuracy: 59.50%\n",
            "Class 0: Sensitivity (Recall): 0.8852, Specificity: 0.4130\n",
            "Class 1: Sensitivity (Recall): 0.5000, Specificity: 0.1667\n",
            "Class 2: Sensitivity (Recall): 0.3000, Specificity: 0.2500\n",
            "Class 3: Sensitivity (Recall): 0.1000, Specificity: 0.7778\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 41, Loss: 0.2512\n",
            "Test Loss: 1.9276, Accuracy: 52.07%\n",
            "Class 0: Sensitivity (Recall): 0.5410, Specificity: 0.3265\n",
            "Class 1: Sensitivity (Recall): 0.7500, Specificity: 0.4444\n",
            "Class 2: Sensitivity (Recall): 0.6500, Specificity: 0.6389\n",
            "Class 3: Sensitivity (Recall): 0.1000, Specificity: 0.7778\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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        },
        {
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          "name": "stdout",
          "text": [
            "Epoch: 42, Loss: 0.2036\n",
            "Test Loss: 1.8680, Accuracy: 51.24%\n",
            "Class 0: Sensitivity (Recall): 0.6557, Specificity: 0.3846\n",
            "Class 1: Sensitivity (Recall): 0.5000, Specificity: 0.4737\n",
            "Class 2: Sensitivity (Recall): 0.3000, Specificity: 0.6250\n",
            "Class 3: Sensitivity (Recall): 0.3000, Specificity: 0.7143\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 43, Loss: 0.2432\n",
            "Test Loss: 1.4052, Accuracy: 54.55%\n",
            "Class 0: Sensitivity (Recall): 0.7869, Specificity: 0.4000\n",
            "Class 1: Sensitivity (Recall): 0.5500, Specificity: 0.4211\n",
            "Class 2: Sensitivity (Recall): 0.3000, Specificity: 0.6250\n",
            "Class 3: Sensitivity (Recall): 0.0500, Specificity: 0.8333\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 44, Loss: 0.1992\n",
            "Test Loss: 1.7148, Accuracy: 52.89%\n",
            "Class 0: Sensitivity (Recall): 0.7705, Specificity: 0.4337\n",
            "Class 1: Sensitivity (Recall): 0.6500, Specificity: 0.4091\n",
            "Class 2: Sensitivity (Recall): 0.1000, Specificity: 0.6667\n",
            "Class 3: Sensitivity (Recall): 0.1000, Specificity: 0.8000\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 45, Loss: 0.1616\n",
            "Test Loss: 1.9114, Accuracy: 52.07%\n",
            "Class 0: Sensitivity (Recall): 0.7869, Specificity: 0.4000\n",
            "Class 1: Sensitivity (Recall): 0.2500, Specificity: 0.5455\n",
            "Class 2: Sensitivity (Recall): 0.3500, Specificity: 0.2222\n",
            "Class 3: Sensitivity (Recall): 0.1500, Specificity: 0.8571\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 46, Loss: 0.1832\n",
            "Test Loss: 2.1460, Accuracy: 55.37%\n",
            "Class 0: Sensitivity (Recall): 0.7213, Specificity: 0.3889\n",
            "Class 1: Sensitivity (Recall): 0.6000, Specificity: 0.4286\n",
            "Class 2: Sensitivity (Recall): 0.3000, Specificity: 0.2500\n",
            "Class 3: Sensitivity (Recall): 0.2500, Specificity: 0.7500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|█████████████████████████████████████████| 120/120 [01:10<00:00,  1.70it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 47, Loss: 0.2471\n",
            "Test Loss: 1.7554, Accuracy: 52.89%\n",
            "Class 0: Sensitivity (Recall): 0.6557, Specificity: 0.3443\n",
            "Class 1: Sensitivity (Recall): 0.6500, Specificity: 0.5185\n",
            "Class 2: Sensitivity (Recall): 0.4000, Specificity: 0.6190\n",
            "Class 3: Sensitivity (Recall): 0.1500, Specificity: 0.7500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|█████████████████████████████████████████| 120/120 [01:10<00:00,  1.70it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 48, Loss: 0.1398\n",
            "Test Loss: 2.0755, Accuracy: 55.37%\n",
            "Class 0: Sensitivity (Recall): 0.7869, Specificity: 0.3924\n",
            "Class 1: Sensitivity (Recall): 0.6500, Specificity: 0.4583\n",
            "Class 2: Sensitivity (Recall): 0.2500, Specificity: 0.3750\n",
            "Class 3: Sensitivity (Recall): 0.0500, Specificity: 0.9000\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|█████████████████████████████████████████| 120/120 [01:10<00:00,  1.70it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 49, Loss: 0.3330\n",
            "Test Loss: 1.6170, Accuracy: 50.41%\n",
            "Class 0: Sensitivity (Recall): 0.5082, Specificity: 0.3542\n",
            "Class 1: Sensitivity (Recall): 0.7000, Specificity: 0.4400\n",
            "Class 2: Sensitivity (Recall): 0.5500, Specificity: 0.5217\n",
            "Class 3: Sensitivity (Recall): 0.2500, Specificity: 0.8000\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|█████████████████████████████████████████| 120/120 [01:10<00:00,  1.70it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 50, Loss: 0.2016\n",
            "Test Loss: 1.9680, Accuracy: 46.28%\n",
            "Class 0: Sensitivity (Recall): 0.4262, Specificity: 0.3810\n",
            "Class 1: Sensitivity (Recall): 0.5500, Specificity: 0.4762\n",
            "Class 2: Sensitivity (Recall): 0.6500, Specificity: 0.4583\n",
            "Class 3: Sensitivity (Recall): 0.3000, Specificity: 0.8235\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Analysis:\n",
        "1. The loss is decreasing. We need more epoch."
      ],
      "metadata": {
        "id": "OmuIiGVBQtsM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#"
      ],
      "metadata": {
        "id": "kr7E1x71HAgW"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}